Create figures for fishery atlas

Author

Max Lindmark

Published

2024-04-18

Load libraries

Code 
library(tidyverse)
library(tidylog)
library(patchwork)
library(viridis)
library(RColorBrewer)
library(ggforce)
library(readxl)
library(ggalluvial)
#library(ggspatial)
library(raster)
library(sp)
# devtools::install_github("seananderson/ggsidekick") # not on CRAN 
library(ggsidekick); theme_set(theme_sleek(base_size = 10))

# Set make working directory relative to your path -- no hardcoded paths
home <- here::here()

# Load functions for plotting and manipulating data
source(paste0(home, "/R/lat-lon.R"))
source(paste0(home, "/R/map-plot.R"))

# Change the limit for scientific notation
options(scipen = 10000)

Load data

  • Making figures in the order they appear in the previous atlas

  • Sometimes I have standardized omitted plots and I have focused more on producing a script that can be updated each year automatically

  • The plotting conventions are as follows (trying to follow Claus Wilke’s Fundamentals of Data Visualization):

    • Trends over time (single level): geom_line(), and also when the trend is not visible with bar or area plot
    • Trends over time (many levels): geom_area() or geom_bar, depending on how smooth it is over time. If smooth, area, if not bar. Bar also for discards because we have gaps in the time series
    • many levels (time periods or single time): geom_col() with facet_zoom() to visualize small groups
    • Seasonal trends: coord_polar()
    • Colors:
      • viridis plasma for map plots
      • gears: ramp up RdYlBu (cluster by fishery)
      • species: ramp up set3 (cluster by family)
      • fisheries: mako (cluster by similarity)
      • years: spectral

  • quarto, renv::renv(), GitHub to ensure reproducibility

  • TODO

    • Ansträngning? Days-at-sea? Over time or within a year?
    • Rerun with high-quality maps
    • Gör även fig. 2.1.10 och 2.1.11?

Långsiktigt: - Gör funktioner för data prep! Typ, get_vessel_size_distribution() - Värden på arter

Code 
# Sofia has now updated it so that it's a single file with the "correct" focat code
load(paste0(home, "/data/test_lovgren_foCatFA.RData"))
lb <- lovgren_foCatFA %>% rename(foCatFA = foCatFA_update)

# Add fisheries common names
fishery_names <- read_delim(paste0(home, "/data/names_fisheries.csv"), delim = ";") %>%
  dplyr::select(-HAVSKOD, -Redskap, -foCatFA_tmp, -foCatEu4)

# See how they match
#setdiff(fishery_names$foCatFA, lb$foCatFA)

lb <- lb %>% left_join(fishery_names, by = "foCatFA")

# TODO: All active non-pelagic gear in the Baltic are not cod-trawl, esp after 2019. Rename?

# Add coordinates
lb <- lb %>%
  mutate(lat = format_position(LATITUD),
         lon = format_position(LONGITUD))

# Now that we have coords, we can trim the map plot function
# High resolution maps for single plots and/or local plots
plot_map_l <- plot_map_l + 
  xlim(min(filter(lb, lat > 50)$lon), max(filter(lb, lat > 50)$lon)) +
  ylim(min(filter(lb, lat > 50)$lat), max(filter(lb, lat > 50)$lat))

# Medium resolution map is for facets and/or for large maps
plot_map_m <- plot_map_m + 
  xlim(min(lb$lon), max(lb$lon)) +
  ylim(min(lb$lat), max(lb$lat))

# Add year & month
lb <- lb %>% 
  mutate(year = year(as.Date(as.character(AVRESDAT), format = "%Y%m%d")),
         month = month(as.Date(as.character(AVRESDAT), format = "%Y%m%d")),
         month_name = as.factor(month),
         month_name = fct_recode(month_name,
                                 "Januari" = "1",
                                 "Februari" = "2",
                                 "Mars" = "3",
                                 "April" = "4",
                                 "Maj" = "5",
                                 "Juni" = "6",
                                 "Juli" = "7",
                                 "Augusti" = "8",
                                 "September" = "9",
                                 "Oktober" = "10",
                                 "November" = "11",
                                 "December" = "12"))

# Add harbour names. For some reason they are in two files... 
harbors <- read_excel(paste0(home, "/data/Code-location-v1.7.xls"), skip = 4) %>% 
  dplyr::select(`ISO-3 Country Code`, `LOCODE Code`, Description, Latitude, Longitude) %>% 
  janitor::clean_names() %>% 
  rename(LANDHAMN_EUKOD = locode_code)

# Join harbour file with coordinates to the landhamn data below (LANDHAMN_EUKOD = LOCODE, SE 999 is NA)
load(paste0(home, "/data/landhamn_2003-2022.RData")) 

# The way to join harbours into the lb data is not via harbour name, but unique activity. That's we we join harbors to landhamn
landhamn <- landhamn %>% left_join(harbors, by = "LANDHAMN_EUKOD")

# Resnummer, loggbladsnummer, aktivitetsnummer, year is an ID for a single "effort". But it's duplicated, but not in any of the columns I can see. Perhaps landed species? Make this ID in both and join. 
landhamn <- landhamn %>%
  mutate(id = paste(RESNR, LOGGBLNR, AKTIVNR, year, sep = "_")) %>%
  distinct(id, .keep_all = TRUE) %>% 
  rename(landhamn_lat = latitude,
         landhamn_lon = longitude) %>% 
  dplyr::select(id, LANDHAMN, landhamn_lat, landhamn_lon)

lb <- lb %>%
  mutate(id = paste(RESNR, LOGGBLNR, AKTIVNR, year, sep = "_")) 

lb <- lb %>% left_join(landhamn, by = "id")

# Efter 2016 finns det olika typer av landnings (för landhamn så blir det ingen match om inte fångsten landats! se t.ex. redskap)

# Add species common and latin names
spcode <- read_delim(paste0(home, "/data/names_species.csv"), delim = ";") %>% 
  dplyr::select(FNAMNSVE, MAFKOD)

# Clean some errors and categories we don't use
lb <- lb %>%
  left_join(spcode, by = "MAFKOD") %>% 
  filter(KVANT > 0) %>% 
  filter(!FNAMNSVE %in% c("Redskap Med Sälskada (St Redskap)",
                          "Obest. benfiskar")) %>%
  drop_na(FNAMNSVE)
 
# Add gear names
# TODO: use Sofia's data? But 2021 does not contain all gears
gear_names <- read_delim(paste0(home, "/data/scrap_names_gear.csv"), delim = ";") %>%
  dplyr::select(-Note)

# gear_names <- read_delim(paste0(home, "/data/names_gear.csv"), delim = ";") %>%
#   dplyr::select(-Note) %>% 
#   mutate(Redskap2 = NA) %>% 
#   mutate(Redskap2 = ifelse(grepl("Parbottentrål", REDSKAP), "Parbottentrål", Redskap2),
#          Redskap2 = ifelse(grepl("Bottentrål", REDSKAP), "Bottentrål", Redskap2),
#          Redskap2 = ifelse(grepl("Btrål", REDSKAP), "Bottentrål", Redskap2),
#          Redskap2 = ifelse(grepl("Flyttrål", REDSKAP), "Flyttrål", Redskap2),
#          Redskap2 = ifelse(grepl("Parflyttrål", REDSKAP), "Parflyttrål", Redskap2)) %>% 
#   as.data.frame()

# gear_names
# 
# unique(gear_names$Redskap2)

lb <- lb %>% left_join(gear_names, by = "REDSKKOD")

# Load discards
discard <- read_excel(paste0(home, "/data/Discard 2008-2022.xlsx")) %>%
  dplyr::select(Year, foCatNat, Art, `Utkast (kg)`) %>% 
  rename(year = Year,
         discards = `Utkast (kg)`)

# Join the total loggbook landings by the same aggregation (year, fishery)
lb_fishery <- lb %>% 
  rename(Art = FNAMNSVE) %>% 
  summarise(KVANT = sum(KVANT), .by = c(foCatNat, year, Art))
  
discard <- discard %>%
  left_join(lb_fishery, by = c("foCatNat", "year", "Art")) %>%
  filter(discards > 0)

# Lastly, join in fishery based on focatnat
discard <- discard %>% 
  left_join(lb %>%
              distinct(foCatNat, .keep_all = TRUE) %>%
              dplyr::select("fishery", "foCatNat", "HAVSKOD"),
            by = "foCatNat") %>% 
  drop_na(fishery)

# Annotation df for discard
md <- expand_grid(year = c(2020, 2021, 2015), 
                  y = 0,
                  fishery = unique(discard$fishery),
                  label = "No discard data collection") %>% 
  mutate(keep = ifelse(year == 2015 & !fishery == "Bottentrålfiske efter torsk i Östersjön", "N", "Y")) %>% 
  filter(keep == "Y") %>% 
  dplyr::select(-keep)

# Add ICES subdivision
shape <- shapefile(paste0(home, "/data/ICES_StatRec_mapto_ICES_Areas/StatRec_map_Areas_Full_20170124.shp"))

pts <- SpatialPoints(cbind(lb$lon, lb$lat), 
                     proj4string = CRS(proj4string(shape)))

lb$subdiv <- over(pts, shape)$Area_27
# TODO: Check Redskap2, did I do it correctly? I think I could also have gotten redskap by joining Fiskeridata_FLEET by "foCatEu4", but then I'd rather make a new file that is distinct with respect to "foCatEu4" and "redskap", because it's unclear to me why it isn't (has year/month but they don't change by that...). Moreover, it seems to miss things. Perhaps it's best to match and get Redskap and then assign that to a category

Entire fleet

Map-plots of fishing activity (landings)

Code 
# For creating hexagon polygons: https://urbandatapalette.com/post/2021-08-tessellation-sf/
# For summing landings by hexagon polygons https://stackoverflow.com/questions/69438693/tallying-variable-for-points-across-polygons-with-sfst-covers

lb_sf <- lb %>%
  st_as_sf(coords = c("lon", "lat"), crs = 4326, remove = FALSE)

area_honeycomb_grid = st_make_grid(lb_sf, 0.5, what = "polygons", square = FALSE)
#area_square_grid = st_make_grid(lb_sf, c(0.15, 0.1), what = "polygons", square = FALSE)

# To sf and add grid ID
honeycomb_grid_sf = st_sf(area_honeycomb_grid) %>%
  mutate(polygon_id = as.character(1:length(lengths(area_honeycomb_grid)))) # add grid ID

polygon_counts <- lb_sf %>%
  mutate(polygon_id = as.character(st_intersects(geometry, honeycomb_grid_sf))) %>%
  group_by(year, polygon_id) %>%
  summarise(sum_kvant = sum(KVANT)) %>%
  st_drop_geometry()

polygon_sum <- honeycomb_grid_sf %>%
  left_join(polygon_counts, by = "polygon_id") %>%
  drop_na(sum_kvant)

# All years but Morocco & Arctic excluded for visibility
landings_map <- plot_map_m +
  geom_sf(data = polygon_sum, aes(fill = sum_kvant/1000), color = NA) +
  scale_fill_viridis(trans = "sqrt", option = "plasma",
                     na.value = "yellow",
                     limits = c(0, quantile(polygon_sum$sum_kvant/1000, 0.99)),
                     name = "Landningar (ton)") +
  geom_sf(color = "gray70") +
  facet_wrap(~year, ncol = 5) +
  xlim(min(filter(lb, lat > 50)$lon), max(filter(lb, lat > 50)$lon)) +
  ylim(min(filter(lb, lat > 50)$lat), 70) +
  guides(fill = guide_colorbar(title.position = "top", title.hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "bottom", 
        legend.title = element_text(size = 6),
        legend.text = element_text(size = 5),
        legend.key.width = unit(1, "cm"),
        legend.key.height = unit(0.3, "cm"))

ggsave(plot = landings_map, paste0(home, "/figures/entire_fleet/landings_map.png"), width = 17, height = 17, units = "cm")

# Now aggregate all years pooled and make a big map plot
polygon_counts_agg <- polygon_counts %>% 
  mutate(tp = ifelse(year > 2015, "2016-2022", "2003-2015")) %>% 
  group_by(tp, polygon_id) %>% 
  summarise(sum_kvant = sum(sum_kvant) / 1000000)

polygon_sum_agg <- honeycomb_grid_sf %>%
  left_join(polygon_counts_agg, by = "polygon_id") %>%
  drop_na(sum_kvant)

landings_map_select <- plot_map_m +
  geom_sf(data = polygon_sum_agg, aes(fill = sum_kvant), color = NA) +
  scale_fill_viridis(trans = "sqrt", option = "plasma",
                     na.value = "yellow", name = "Landningar (tusen ton)",
                     limits = c(0, quantile(polygon_sum_agg$sum_kvant, 0.99))) +
  geom_sf(color = "gray70") +
  facet_wrap(~tp) + 
  theme(axis.text.x = element_text(angle = 0),
        legend.position = c(0.12, 0.9))

ggsave(plot = landings_map_select, paste0(home, "/figures/entire_fleet/landings_map_select.png"), width = 17, height = 17, units = "cm")

Landings-quantity by landing harbour

Code 
# Make also a table here... 
land_quant_new <- lb %>%
  filter(year > 2015) %>% 
  summarise(kvant_sum = sum(KVANT) / 1000000, .by = LANDHAMN) %>% 
  left_join(lb %>%
              distinct(LANDHAMN, .keep_all = TRUE) %>%
              dplyr::select(landhamn_lat, landhamn_lon, LANDHAMN),
            by = "LANDHAMN") %>% 
  drop_na(landhamn_lat, landhamn_lon)

land_quant_old <- lb %>%
  filter(year < 2016) %>% 
  summarise(kvant_sum = sum(KVANT) / 1000000, .by = LANDHAMN) %>% 
  left_join(lb %>%
              distinct(LANDHAMN, .keep_all = TRUE) %>%
              dplyr::select(landhamn_lat, landhamn_lon, LANDHAMN),
            by = "LANDHAMN") %>% 
  drop_na(landhamn_lat, landhamn_lon)

land_plot <- plot_map_m + 
  scale_color_gradient(high = "#A50026", low = "#313695") + # These are from RdYlBu in RColorBrewer
  scale_size(range = c(0.2, 6)) + 
  xlim(min(c(land_quant_old$landhamn_lon,
             land_quant_new$landhamn_lon)),
       max(c(land_quant_old$landhamn_lon,
             land_quant_new$landhamn_lon))) +
  ylim(min(c(land_quant_old$landhamn_lat,
             land_quant_new$landhamn_lat)),
       max(c(land_quant_old$landhamn_lat,
             land_quant_new$landhamn_lat))) +
  labs(color = "Landningar (ton)", size = "") +
  guides(size = "none", color = guide_colourbar(title.position = "top", title.hjust = 0.5)) +
  theme_sleek(base_size = 9) +
  theme(legend.position = c(0.5, 0.9),
        legend.direction = "horizontal",
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 6),
        legend.key.width = unit(0.5, "cm"),
        legend.key.height = unit(0.15, "cm"))
  
p1 <- land_plot +
  geom_point(data = land_quant_old, aes(landhamn_lon, landhamn_lat, color = kvant_sum, size = kvant_sum),
             alpha = 0.4) +
  annotate("text", label = paste0("Medel=", round(mean(land_quant_old$kvant_sum)), " ton"),
           x = -Inf, y = Inf, hjust = -0.06, vjust = 2, color = "gray20", size = 2) +
  annotate("text", label = "År 2003-2015",
           x = -Inf, y = Inf, hjust = -0.1, vjust = 4, color = "gray20", size = 2)

p2 <- land_plot +
  geom_point(data = land_quant_new, aes(landhamn_lon, landhamn_lat, color = kvant_sum, size = kvant_sum),
             alpha = 0.4) + 
  annotate("text", label = paste0("Medel=", round(mean(land_quant_new$kvant_sum)), " ton"),
           x = -Inf, y = Inf, hjust = -0.06, vjust = 2, color = "gray20", size = 2) +
  annotate("text", label = "År 2016-2022",
           x = -Inf, y = Inf, hjust = -0.1, vjust = 4, color = "gray20", size = 2)

landings_harbour <- p1 + p2

ggsave(plot = landings_harbour, paste0(home, "/figures/entire_fleet/landings_harbour.png"), width = 20, height = 8, units = "cm")

Fig. 1.4.1 Utbredning av fiskeplatser (rapporterade landningar) i svenska yrkesfisket 2003–2022.

Fig. 1.4.1 Utbredning av fiskeplatser (rapporterade landningar) i svenska yrkesfisket för åren 2003-2015 (vänster) samt för 2016-2022 (höger)

Fig. 1.4.1 Utbredning av landningshamnnar (rapporterade landningar) i svenska yrkesfisket för åren 2003-201 (vänster) samt för 2016-2022 (höger)

Match catches to landings. Note we only match unique hauls for 2022, else to crowded

Code 
# Now match catches to landings
link <- lb %>%
  filter(year == 2022) %>%
  drop_na(landhamn_lon) %>%
  mutate(sum_kvant = sum(KVANT), .by = c(id, year)) %>% 
  distinct(id, .keep_all = TRUE)
  
p1 <- plot_map_m +
  geom_curve(data = link %>% filter(FNAMNSVE == "Skarpsill"),
             aes(x = lon, xend = landhamn_lon, y = lat, yend = landhamn_lat,
                 color = sum_kvant/1000),
             curvature = -0.15, angle = 90, alpha = 0.1, linewidth = 0.1) + #color = "grey20") +
  geom_point(data = link %>% filter(FNAMNSVE == "Skarpsill"),
             aes(landhamn_lon, landhamn_lat), size = 0.01, color = "#A50026") +
  coord_sf(xlim = c(min(c(link$landhamn_lon, link$lon)),
                    max(c(link$landhamn_lon, link$lon))),
           ylim = c(min(c(link$landhamn_lat, link$lat)),
                    max(c(link$landhamn_lat, link$lat)))) + 
  scale_color_viridis(trans = "log10", breaks = c(0.1, 1, 10, 100),
                      option = "mako", name = "Ton", direction = -1) +
  theme(legend.position = c(0.1, 0.85),
        legend.key.height = unit(0.25, "cm"),
        legend.key.width = unit(0.2, "cm"),
        legend.title = element_text(size = 7))

p2 <- plot_map_l +
  geom_curve(data = link,
             aes(x = lon, xend = landhamn_lon, y = lat, yend = landhamn_lat,
                 color = sum_kvant/1000),
             curvature = -0.15, angle = 90, alpha = 0.1, linewidth = 0.07) + #color = "grey20") +
  geom_point(data = link %>% filter(FNAMNSVE == "Skarpsill"),
             aes(landhamn_lon, landhamn_lat), size = 0.05, color = "#A50026") +
  coord_sf(xlim = c(10, 12.5), ylim = c(56.7, 59)) + 
  scale_color_viridis(trans = "log10", breaks = c(0.1, 1, 10, 100),
                      option = "mako", name = "Ton", direction = -1) +
  guides(color = "none") + 
  labs(y = "", x = "")

landings_map_arrow <- p1 + p2

ggsave(plot = landings_map_arrow, 
       paste0(home, "/figures/entire_fleet/landings_map_arrow.png"), width = 17, height = 10, units = "cm")


# By ICES sub division instead?
link_agg <- lb %>%
  filter(year == 2022) %>%
  drop_na(landhamn_lon) %>%
  filter(KVANT > 0) %>%
  summarise(sum_kvant = sum(KVANT), .by = c(year, subdiv, LANDHAMN))

# Join in sub div coordinates
sub_div_coord <- lb %>% 
  drop_na(subdiv) %>% 
  summarise(lon2 = mean(lon), lat2 = mean(lat), .by = subdiv)

link_agg <- link_agg %>% 
  left_join(sub_div_coord, by = "subdiv")

# Join in landhamn coordinates
land_coord <- lb %>% 
  distinct(LANDHAMN, .keep_all = TRUE) %>% 
  dplyr::select(LANDHAMN, landhamn_lon, landhamn_lat)

link_agg <- link_agg %>% 
  left_join(land_coord, by = "LANDHAMN")

p1 <- plot_map_m +
  geom_curve(data = link_agg,
             aes(x = lon2, xend = landhamn_lon, y = lat2, yend = landhamn_lat,
                 color = sum_kvant/1000, linewidth = sum_kvant/1000),
             curvature = -0.15, angle = 90, alpha = 0.4,
             arrow = arrow(length = unit(0.1, "cm"), 
                           type = "closed")) + 
  geom_point(data = link_agg,
             aes(landhamn_lon, landhamn_lat), size = 0.01, color = "#A50026") +
  scale_linewidth(range = c(0.1, 3.5)) + 
  coord_sf(xlim = c(min(c(link$landhamn_lon, link$lon)),
                    max(c(link$landhamn_lon, link$lon))),
           ylim = c(min(c(link$landhamn_lat, link$lat)),
                    max(c(link$landhamn_lat, link$lat)))) +
  scale_color_viridis(trans = "log10", breaks = c(0.1, 1, 10, 100, 1000, 10000),
                      option = "mako", name = "Ton", direction = -1) +
  guides(linewidth = "none") +
  theme(legend.position = c(0.1, 0.85),
        legend.key.height = unit(0.25, "cm"),
        legend.key.width = unit(0.2, "cm"),
        legend.title = element_text(size = 7))

p2 <- plot_map_l +
  geom_curve(data = link_agg,
             aes(x = lon2, xend = landhamn_lon, y = lat2, yend = landhamn_lat,
                 color = sum_kvant/1000, linewidth = sum_kvant/1000),
             curvature = -0.15, angle = 90, alpha = 0.4,
             arrow = arrow(length = unit(0.08, "cm"), 
                           type = "closed")) + 
  scale_linewidth(range = c(0.1, 3.5)) + 
  geom_point(data = link,
             aes(landhamn_lon, landhamn_lat), size = 0.05, color = "#A50026") +
  coord_sf(xlim = c(10, 12.5), ylim = c(56.7, 59)) + 
  scale_color_viridis(trans = "log10", breaks = c(0.1, 1, 10, 100),
                      option = "mako", name = "Ton", direction = -1) +
  guides(color = "none",
         linewidth = "none") + 
  labs(y = "", x = "")

landings_map_arrow <- p1 + p2

ggsave(plot = landings_map_arrow, 
       paste0(home, "/figures/entire_fleet/landings_map_arrow_subdiv.png"), width = 17, height = 10, units = "cm")

Same as above but with numbers of fishing activities. Note this requires some aggregation.

Code 
link_agg <- lb %>%
  filter(year == 2022) %>%
  drop_na(landhamn_lon) %>%
  summarise(n_trips = n(), .by = c(year, subdiv, LANDHAMN))

# Join in sub div coordinates
sub_div_coord <- lb %>% 
  drop_na(subdiv) %>% 
  summarise(lon2 = mean(lon), lat2 = mean(lat), .by = subdiv)

link_agg <- link_agg %>% 
  left_join(sub_div_coord, by = "subdiv")

# Join in landhamn coordinates
land_coord <- lb %>% 
  distinct(LANDHAMN, .keep_all = TRUE) %>% 
  dplyr::select(LANDHAMN, landhamn_lon, landhamn_lat)

link_agg <- link_agg %>% 
  left_join(land_coord, by = "LANDHAMN")

p1 <- plot_map_m +
  geom_curve(data = link_agg,
             aes(x = lon2, xend = landhamn_lon, y = lat2, yend = landhamn_lat,
                 color = n_trips, linewidth = n_trips),
             curvature = -0.15, angle = 90, alpha = 0.4,
             arrow = arrow(length = unit(0.1, "cm"), 
                           type = "closed")) + 
  geom_point(data = link_agg,
             aes(landhamn_lon, landhamn_lat), size = 0.01, color = "#A50026") +
  scale_linewidth(range = c(0.3, 2)) + 
  coord_sf(xlim = c(min(c(link$landhamn_lon, link$lon)),
                    max(c(link$landhamn_lon, link$lon))),
           ylim = c(min(c(link$landhamn_lat, link$lat)),
                    max(c(link$landhamn_lat, link$lat)))) +
  scale_color_viridis(trans = "log10", breaks = c(0.1, 1, 10, 100, 1000, 10000),
                      option = "mako", name = "Antal ansträngningar", direction = -1) +
  guides(linewidth = "none") +
  theme(legend.position = c(0.18, 0.85),
        legend.key.height = unit(0.25, "cm"),
        legend.key.width = unit(0.2, "cm"),
        legend.title = element_text(size = 7))

p2 <- plot_map_l +
  geom_curve(data = link_agg,
             aes(x = lon2, xend = landhamn_lon, y = lat2, yend = landhamn_lat,
                 color = n_trips, linewidth = n_trips),
             curvature = -0.15, angle = 90, alpha = 0.4,
             arrow = arrow(length = unit(0.08, "cm"), 
                           type = "closed")) + 
  scale_linewidth(range = c(0.3, 2)) + 
  geom_point(data = link,
             aes(landhamn_lon, landhamn_lat), size = 0.05, color = "#A50026") +
  coord_sf(xlim = c(10, 12.5), ylim = c(56.7, 59)) + 
  scale_color_viridis(trans = "log10", breaks = c(0.1, 1, 10, 100),
                      option = "mako", name = "Antal ansträngningar", direction = -1) +
  guides(color = "none",
         linewidth = "none") + 
  labs(y = "", x = "")

trips_map_arrow_subdiv <- p1 + p2

ggsave(plot = trips_map_arrow_subdiv, 
       paste0(home, "/figures/entire_fleet/trips_map_arrow_subdiv.png"), width = 17, height = 10, units = "cm")

Fig. x Fångster kopplade till landningar (1 linje = 1 ansträngning)

Fig. x Fångster kopplade till landningar (1 linje = 1 ansträngning), aggregerat på sub division

Fig. x Antal ansträngningar aggregerat på sub division

Sea of catch and country landed

On a summarized level, where are fish caught and where are fish landed?

Code 
spec_sub <- lb %>%
  summarise(sum_kvant = sum(KVANT), .by = FNAMNSVE) %>%
  arrange(desc(sum_kvant)) %>%
  drop_na() %>%
  slice(1:10)

landland_sub <- lb %>%
  summarise(sum_kvant = sum(KVANT), .by = LANDLAND) %>%
  arrange(desc(sum_kvant)) %>%
  drop_na() %>%
  slice(1:8) %>% 
  filter(!LANDLAND == "MAR")

alluvial <- lb %>%
  summarise(sum_kvant = sum(KVANT), .by = c(FNAMNSVE, HAVSKOD, LANDLAND)) %>%
  filter(FNAMNSVE %in% unique(spec_sub$FNAMNSVE)) %>%
  filter(LANDLAND %in% unique(landland_sub$LANDLAND)) %>%
  drop_na()

# Additional cleaning
alluvial <- alluvial %>%
  #filter(!fishery2 == "Fiske efter lax") %>% 
  mutate(HAVSKOD2 = ifelse(HAVSKOD == "ÖN", "Norra\nÖstersjön", NA),
         HAVSKOD2 = ifelse(HAVSKOD == "ÖS", "Södra\nÖstersjön", HAVSKOD2),
         HAVSKOD2 = ifelse(HAVSKOD == "K", "Kattegatt", HAVSKOD2),
         HAVSKOD2 = ifelse(HAVSKOD == "S", "Skagerrak", HAVSKOD2),
         HAVSKOD2 = ifelse(HAVSKOD == "N", "Nordsjön", HAVSKOD2),
         HAVSKOD2 = ifelse(HAVSKOD == "AN", "Norska Havet", HAVSKOD2))

sea_species_landings <- 
  ggplot(alluvial,
         aes(axis1 = LANDLAND, axis2 = FNAMNSVE, axis3 = HAVSKOD2,
             y = sqrt(sum_kvant))) +
  geom_alluvium(aes(fill = FNAMNSVE, color = FNAMNSVE), curve_type = "cubic") +
  geom_stratum(size = 0.5) +
  geom_text(stat = "stratum", size = 3,
            aes(label = after_stat(stratum))) +
  guides(fill = "none") +
  scale_fill_brewer(palette = "Paired", name = "") +
  scale_color_brewer(palette = "Paired", name = "") +
  theme_void(base_size = 1) +
  guides(color = "none") +
  theme(panel.background = element_rect(fill = "white"))

ggsave(plot = sea_species_landings, paste0(home, "/figures/entire_fleet/sea_species_landings.png"), width = 20, height = 27, units = "cm")

# Plotting sqrt transformation
# test %>% 
#   summarise(sum_kvant = sum(sum_kvant), .by = FNAMNSVE) %>% 
#   mutate(sqrt_sum_kvant = sqrt(sum_kvant)) %>% 
#   pivot_longer(c("sqrt_sum_kvant", "sum_kvant")) %>% 
#   ggplot(aes(value, reorder(FNAMNSVE, value))) + 
#   geom_bar(stat = "identity") + 
#   facet_wrap(~name, ncol = 1, scales = "free_x")

Fig. x Havsområde, art, landningsland. Topp 10 arter, och för dessa, topp 7 landningsländer

Cumulative number of harbours

Code 
n_harbours <- lb %>%
  summarise(n_harb = length(unique(LANDHAMN)),
            n_vessel = length(unique(BATNAMN)), 
            .by = year) %>% 
  mutate(n_harb_scaled = n_harb / n_vessel)

p1 <- ggplot(n_harbours, aes(year, n_harb)) +
  geom_line(color = "gray40") + 
  labs(x = "År", y = "Antal landningshamnar")

p2 <- ggplot(n_harbours, aes(year, n_harb_scaled)) +
  geom_line(color = "gray40") + 
  labs(x = "År", y = "Antal landningshamnar relativt antal fartyg")

harbors <- p1 / p2 + plot_layout(axes = "collect")

ggsave(plot = harbors, "figures/entire_fleet/harbors.png", width = 17, height = 15, units = "cm")

cumsum_harb <- lb %>% 
  summarise(kvant_sum = sum(KVANT),
            .by = c(year, LANDHAMN)) %>% 
  arrange(year, desc(kvant_sum)) %>% 
  mutate(cum_sum = cumsum(kvant_sum), 
         year_tot = sum(kvant_sum),
         cum_sum_prop = cum_sum / year_tot,
         n = row_number(),
         .by = year)

ogives_harb <- cumsum_harb %>%
  filter(year %in% c(2003, 2022) & cum_sum_prop < 0.8) %>%
  group_by(year) %>%
  filter(cum_sum_prop == max(cum_sum_prop))

# Here I need a color that has visible colors in both extreme ends
nyears <- length(unique(cumsum_harb$year))
colors <- colorRampPalette(brewer.pal(name = "Spectral", n = 10))(nyears)

p1 <- ggplot(cumsum_harb, aes(n, cum_sum_prop, color = factor(year))) + 
  geom_line(alpha = 0.9) + 
  scale_color_manual(values = colors, name = "") +
  labs(y = "Kumulativ andel", x = "") + 
  theme_sleek(base_size = 9) +
  theme(legend.position = c(0.65, 0.1),
        legend.direction = "horizontal",
        legend.spacing.y = unit(0, 'cm'),
        legend.spacing.x = unit(0, 'cm')) + 
  guides(color = guide_legend(nrow = 2))

p2 <- ggplot(cumsum_harb, aes(n, cum_sum_prop, color = factor(year))) + 
  geom_hline(yintercept = 0.8, alpha = 0.5, linetype = 2) + 
  geom_line(alpha = 0.9) + 
  xlim(0, 65) +
  geom_segment(data = ogives_harb, aes(x = n, xend = n, y = 0, yend = cum_sum_prop, color = factor(year)),
               alpha = 0.9, linetype = 2) +
  scale_color_manual(values = colors, name = "") +
  labs(y = "Kumulativ andel", x = "Antal landningshamnar") + 
  theme_sleek(base_size = 9) +
  theme(legend.position = "bottom") + 
  guides(color = "none") + 
  coord_cartesian(expand = 0)

cumulative_harbour <- p1 / p2

ggsave(plot = cumulative_harbour, "figures/entire_fleet/cumulative_harbour.png", width = 17, height = 15, units = "cm")

Fig. x Antal landningshamnar (totalt och relativt till antal fartyg)

Fig. x Kumulativ andel av landningsvikt för det svenska yrkesfisket per år och landningshamn. De vertikala linjerna i den nedre panelen visar det antal hamnar som tar emot 80% av landningsvikten för åren 2003 (röd) och 2022 (blå).
Code 
# Tables

# 2016-2022
knitr::kable(lb %>%
               filter(year > 2015) %>% 
               summarise(kvant_sum = sum(KVANT) / 1000, .by = LANDHAMN) %>% 
               arrange(desc(kvant_sum)) %>% 
               rename(Hamn = LANDHAMN, "Total landings (tonnes)" = kvant_sum) %>% 
               slice(1:10),
             format = "html",
             full_width = FALSE, 
             caption = "Top 10 harbours by landings weight in 2016-2002")
Top 10 harbours by landings weight in 2016-2002
Hamn Total landings (tonnes)
Skagen 449588.71
Västervik 108606.20
Hanstholm 104095.13
Norrsundet 92252.13
Simrishamn 63332.73
Thyborøn 47794.64
Grenaa 41713.18
Rönnäng 40923.55
Ellös 39448.01
Ronehamn 38107.62
Code 
# 2003-2015
knitr::kable(lb %>%
               filter(year < 2016) %>% 
               summarise(kvant_sum = sum(KVANT) / 1000, .by = LANDHAMN) %>% 
               arrange(desc(kvant_sum)) %>% 
               rename(Hamn = LANDHAMN, "Total landings (tonnes)" = kvant_sum) %>% 
               slice(1:10),
             format = "html",
             full_width = FALSE, 
             caption = "Top 10 harbours by landings weight in 2003-2015")
Top 10 harbours by landings weight in 2003-2015
Hamn Total landings (tonnes)
Skagen 1023339.81
Västervik 267871.61
Grenå 262743.62
Simrishamn 224911.05
Rönnäng 130438.70
Nogersund 107343.86
Ellös 74263.26
Hanstholm 62055.61
Ronehamn 60560.81
Karlskrona-Saltö 52106.08

Number of trips over time by fisheries heatmap

Code 
trip_trends <- lb %>% 
  #filter(year > 2015) %>% 
  drop_na(fishery) %>% 
  summarise(n_trips = length(unique(id)), .by = c(year, fishery)) %>% 
  mutate(mean_trips = mean(n_trips), .by = c(fishery)) %>% 
  mutate(scaled_mean = n_trips / mean_trips)

order <- trip_trends %>% 
  filter(year == 2022) %>% 
  arrange(scaled_mean)

trip_heat <-
  ggplot(trip_trends, aes(year, factor(fishery, levels = order$fishery),
                                   fill = scaled_mean)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(midpoint = 1) + 
  coord_cartesian(expand = 0) +
  labs(y = "", x = "År", fill = "Antal ansträngningar relativt\ntill medel för åren 2016-2022 per fiske") + 
  guides(fill = guide_colorbar(title.position = "top", title.hjust = 0.5)) +
  theme(legend.position = "bottom", 
        legend.key.width = unit(1, "cm"),
        legend.key.height = unit(0.3, "cm"))

ggsave(plot = trip_heat, "figures/entire_fleet/trip_heat.png", width = 17, height = 9, units = "cm")

Fig. x Trender i antal ansträngninar

Average catch per trip

Code 
cath_per_trip_trends <- lb %>% 
  #filter(year > 2015) %>% 
  drop_na(fishery) %>% 
  # mean catch by trip id, year and fishery
  summarise(mean_kvant = mean(KVANT), .by = c(year, fishery, id)) %>% 
  # average by year and fishery 
  summarise(mean_kvant = mean(mean_kvant), .by = c(year, fishery)) %>% 
  # scale relative to mean within fishery to make it proportional
  mutate(mean_all_yr = mean(mean_kvant), .by = fishery) %>% 
  mutate(scaled_mean = mean_kvant / mean_all_yr)

order <- cath_per_trip_trends %>% 
  filter(year == 2022) %>% 
  arrange(scaled_mean)

mean_land_trip_heat <-
  ggplot(cath_per_trip_trends, aes(year, factor(fishery, levels = rev(order$fishery)),
                                   fill = scaled_mean)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(midpoint = 1) + 
  coord_cartesian(expand = 0) +
  labs(y = "", x = "År", fill = "Medelfångst per ansträngning relativt\ntill medel för åren 2016-2022 per fiske") + 
  guides(fill = guide_colorbar(title.position = "top", title.hjust = 0.5)) +
  theme(legend.position = "bottom", 
        legend.key.width = unit(1, "cm"),
        legend.key.height = unit(0.3, "cm"))

ggsave(plot = mean_land_trip_heat, "figures/entire_fleet/mean_land_trip_heat.png", width = 17, height = 9, units = "cm")

Fig. x Trend i medelfångst per tur över tid relativt till medel i fisket

Size distribution of fleet

Code 
pal <- brewer.pal(name = "Set1", n = 9)

lb_vessel <- lb %>%
  drop_na(LÄNGD) %>% 
  filter(LÄNGD > 0) %>% 
  summarise(n_vessels = length(unique(BATNAMN)), .by = c(year, LÄNGD)) %>% 
  uncount(n_vessels)
  
lb_vessel_count <- lb %>%
  drop_na(LÄNGD) %>% 
  filter(LÄNGD > 0) %>% 
  summarise(n_vessels = length(unique(BATNAMN)), .by = year) %>% 
  mutate(lab = paste0("n = ", n_vessels))

p1 <- ggplot(lb_vessel, aes(LÄNGD)) + 
  geom_histogram() + 
  facet_wrap(~year) + 
  labs(x = "Längd (m)", y = "Antal fartyg") +
  geom_text(data = lb_vessel_count, aes(Inf, Inf, label = lab), 
            hjust = 1.2, vjust = 2, color = "gray20", size = 2) + 
  coord_cartesian(expand = 0) + 
  theme_sleek(base_size = 9)

change <- lb %>% 
  drop_na(LÄNGD) %>% 
  filter(LÄNGD > 0) %>% 
  mutate(LÄNGD = round(LÄNGD)) %>% 
  mutate(LÄNGD_cat = cut(LÄNGD, breaks = seq(0, max(LÄNGD), by = 3)),
         LÄNGD_cat = str_remove(LÄNGD_cat, "\\("),
         LÄNGD_cat = str_remove(LÄNGD_cat, "\\]"),
         LÄNGD_cat = str_replace(LÄNGD_cat, ",", "-"),
         LÄNGD_cat = ifelse(is.na(LÄNGD_cat), "64-67", LÄNGD_cat)) %>% 
  mutate(tp = ifelse(year < 2016, "old", "new")) %>% 
  summarise(n_vessels = length(unique(BATNAMN)), .by = c(tp, LÄNGD_cat)) %>% 
  pivot_wider(names_from = tp, values_from = n_vessels) %>% 
  mutate(`Förändring (antal)` = new - old,
         `Förändring (procent)` = ((new - old) / old) * 100,
         diff_cat = ifelse(`Förändring (antal)` > 0, "increasing", "decreasing")) %>% 
  pivot_longer(c("Förändring (antal)", "Förändring (procent)")) %>% 
  separate_wider_delim("LÄNGD_cat", delim = "-", names = c("min_LÄNGD", "max_LÄNGD"),
                       cols_remove = FALSE) %>% 
  mutate(min_LÄNGD = as.numeric(min_LÄNGD))

p2 <- ggplot(data = change, 
             aes(x = reorder(LÄNGD_cat, min_LÄNGD), xend = reorder(LÄNGD_cat, min_LÄNGD),
                 y = 0, yend = value, color = diff_cat)) + 
  geom_hline(yintercept = 0, alpha = 0.5, linetype = 2, linewidth = 0.3) + 
  geom_segment(arrow = arrow(length = unit(0.02, "npc"))) +
  #geom_segment() +
  #geom_point(aes(reorder(LÄNGD_cat, min_LÄNGD), value), size = 0.6) +
  scale_fill_manual(values = alpha(pal[1:2], 0.1)) +
  scale_color_manual(values = c(pal[1], pal[3])) + 
  facet_wrap(~name, scales = "free") + 
  labs(y = "2016:2022 - 2003:2015",
       x = "Längdgrupp (m)") + 
  guides(color = "none", fill = "none") +
  theme_sleek(base_size = 9) &
  theme(axis.text.x = element_text(angle = 90, size = 5))

trend <- lb %>% 
  drop_na(LÄNGD) %>% 
  filter(LÄNGD > 0) %>% 
  mutate(size_gr = ifelse(LÄNGD >= 10, ">=10 m", "<10 m")) %>% 
  summarise(n_vessels = length(unique(BATNAMN)), .by = c(year, size_gr))

p3 <- ggplot(trend, aes(year, n_vessels, fill = size_gr)) + 
  geom_area() +
  coord_cartesian(expand = 0) +
  scale_fill_manual(values = c("grey40", "grey70"), name = "Fartygslängd") + 
  labs(y = "Antal fartyg", x = "År") + 
  theme_sleek(base_size = 9) + 
  theme(legend.key.size = unit(0.3, "cm"))

vessel_size_dist <- (p1 / (p2 + p3 + plot_layout(widths = c(1.5, 1)))) + plot_layout(heights = c(2.5, 1)) & 
  theme(legend.position = c(0.87, 0.87),
        legend.text = element_text(size = 5),
        legend.title = element_text(size = 6),
        legend.spacing.y = unit(0, 'cm'),
        legend.spacing.x = unit(0, 'cm'))

ggsave(plot = vessel_size_dist, paste0(home, "/figures/entire_fleet/vessel_size_dist.png"), width = 17, height = 19, units = "cm")

Fig. 1.4.2–1.4.3 Längdfördelning av fartyg den svenska fiskeflottan per år (överst), förändring över tid i andel och procent i fartyg per storleksklass (nedre vänster) samt förändring över tid totalt över alla storlekar, över och under 10m (nedre höder)
# TODO: Plots and results on capacity

Fleet size

Code 
lb_fish_ves <- lb %>% 
  mutate(tp = ifelse(year < 2016, "2003-2015", "2016-2022")) %>% 
  summarise(n_vessel = length(unique(BATNAMN)), .by = c(tp, fishery)) %>% 
  drop_na() %>% 
  pivot_wider(values_from = "n_vessel", names_from = "tp") %>% 
  mutate(trend = ifelse(`2016-2022` > `2003-2015`, "decreasing", "increasing"))

fishery_vessel <- ggplot(lb_fish_ves, aes(`2016-2022`, reorder(fishery, `2016-2022`))) + 
  geom_bar(aes(`2003-2015`, reorder(fishery, `2016-2022`)),
           stat = "identity", fill = "grey90") +
  geom_bar(stat = "identity", fill = "grey60") +
  geom_segment(aes(xend = `2016-2022`, x = `2003-2015`, y = reorder(fishery, `2016-2022`),
                   color = trend),
               arrow = arrow(length = unit(0.02, "npc"))) +
  scale_color_manual(values = c(pal[3], pal[1])) + 
  coord_cartesian(expand = 0) +
  guides(color = "none") +
  labs(x = "Antal fartyg", y = "")

ggsave(plot = fishery_vessel, paste0(home, "/figures/entire_fleet/fishery_vessels.png"), width = 17, height = 8, units = "cm")

Fig. 1.4.12 Antal fartyg per fiske i det svenska yrkesfisket på kusten och i havet för 2003-2015 (ljus) och 2016-2022 (mörk). Pilar indikerar skillnad och riktning

Cumulative share of landings

Code 
cumsum <- lb %>% 
  summarise(kvant_sum = sum(KVANT),
            .by = c(year, BATNAMN)) %>% 
  arrange(year, desc(kvant_sum)) %>% 
  mutate(cum_sum = cumsum(kvant_sum), 
         year_tot = sum(kvant_sum),
         cum_sum_prop = cum_sum / year_tot,
         n = row_number(),
         .by = year)

ogives <- cumsum %>%
  filter(year %in% c(2003, 2022) & cum_sum_prop < 0.8) %>% 
  group_by(year) %>% 
  filter(cum_sum_prop == max(cum_sum_prop))

# Here I need a color that has visible colors in both extreme ends
nyears <- length(unique(cumsum$year))
colors <- colorRampPalette(brewer.pal(name = "Spectral", n = 10))(nyears)

p1 <- ggplot(cumsum, aes(n, cum_sum_prop, color = factor(year))) + 
  geom_line(alpha = 0.9) + 
  scale_color_manual(values = colors, name = "") +
  labs(y = "Kumulativ andel", x = "") + 
  theme_sleek(base_size = 9) +
  theme(legend.position = c(0.65, 0.1),
        legend.direction = "horizontal",
        legend.spacing.y = unit(0, 'cm'),
        legend.spacing.x = unit(0, 'cm')) + 
  guides(color = guide_legend(nrow = 2))

p2 <- ggplot(cumsum, aes(n, cum_sum_prop, color = factor(year))) + 
  geom_hline(yintercept = 0.8, alpha = 0.5, linetype = 2) + 
  geom_line(alpha = 0.9) + 
  xlim(0, 65) +
  geom_segment(data = ogives, aes(x = n, xend = n, y = 0, yend = cum_sum_prop, color = factor(year)),
               alpha = 0.9, linetype = 2) +
  scale_color_manual(values = colors, name = "") +
  labs(y = "Kumulativ andel", x = "Antal fartyg") + 
  theme_sleek(base_size = 9) +
  theme(legend.position = "bottom") + 
  guides(color = "none") + 
  coord_cartesian(expand = 0)

cumulative_landings <- p1 / p2

ggsave(plot = cumulative_landings, "figures/entire_fleet/cumulative_landings.png", width = 17, height = 15, units = "cm")

Fig. 1.4.4 Kumulativ andel av landningsvikt för det svenska yrkesfisket per år. De vertikala linjer i den nedre panelen visar det antal fartyg som fiskar in 80% av landningsvikten för åren 2003 (röd) och 2022 (blå).

Landings by gear

Code 
lb_gear <- lb %>% 
  drop_na(Redskap2) %>% 
  summarise(sum_kvant = sum(KVANT), .by = c(year, Redskap2))

pal_long <- colorRampPalette(brewer.pal(name = "RdYlBu", n = 11))(length(unique(lb_gear$Redskap2)))
  
order <- lb_gear %>% 
  summarise(sum_kvant_all = sum(sum_kvant), .by = Redskap2) %>% 
  arrange(desc(sum_kvant_all)) %>% 
  mutate(sum = sum(sum_kvant_all),
         percent = 100 * (sum_kvant_all / sum)) %>% 
  mutate(group = ifelse(percent > 1, ">1% av totala landningarna", "<1% av totala landningarna"))

lb_gear <- lb_gear %>% left_join(order, by = "Redskap2")

p1 <- ggplot(lb_gear %>% filter(group == ">1% av totala landningarna"),
       aes(year, sum_kvant/1000000, fill = Redskap2, order = sum_kvant)) +
  #geom_bar(stat = "identity") +
  geom_area() +
  scale_fill_manual(values = pal_long, name = "") + 
  theme_sleek(base_size = 9) +
  theme(legend.position = c(0.8, 0.93),
        legend.direction = "horizontal",
        legend.spacing.y = unit(0, 'cm'),
        legend.spacing.x = unit(0.2, 'cm'),
        legend.key.size = unit(0.4, 'cm')) +
  labs(y = "Landningar (tusen ton)", x = "") + 
  guides(fill = guide_legend(nrow = 2)) +
  coord_cartesian(expand = 0) + 
  facet_wrap(~group)

p2 <- ggplot(lb_gear %>% filter(group == "<1% av totala landningarna"),
       aes(year, sum_kvant/1000000, fill = Redskap2, order = sum_kvant)) +
  #geom_bar(stat = "identity") +
  geom_area() +
  scale_fill_manual(values = rev(pal_long), name = "") + 
  theme_sleek(base_size = 9) +
  theme(legend.position = c(0.68, 0.93),
        legend.direction = "horizontal",
        legend.spacing.y = unit(0, 'cm'),
        legend.spacing.x = unit(0.2, 'cm'),
        legend.key.size = unit(0.4, 'cm')) +
  labs(y = "Landningar (tusen ton)", x = "År") + 
  guides(fill = guide_legend(nrow = 2)) +
  coord_cartesian(expand = 0) + 
  facet_wrap(~group)

landings_gear <- p1 / p2
  
ggsave(plot = landings_gear, "figures/entire_fleet/landings_gear.png", width = 17, height = 20, units = "cm")

Fig. 1.4.5 Redskap som används i det svenska yrkesfisket på kusten och i havet över tid. Andelen är beräknat på landningsvikt.
# TODO: Days-at-at by gear?

Landings by species

Code 
lb_spec <- lb %>% 
  drop_na(FNAMNSVE) %>% 
  filter(year > 2015) %>% 
  summarise(sum_kvant = sum(KVANT), .by = c(year, FNAMNSVE))

order <- lb_spec %>% 
  summarise(sum_kvant_all = sum(sum_kvant), .by = FNAMNSVE) %>% 
  arrange(desc(sum_kvant_all)) %>% 
  mutate(sum = sum(sum_kvant_all),
         percent = 100 * (sum_kvant_all / sum))

lb_spec <- lb_spec %>% left_join(order, by = "FNAMNSVE")

landings_top_12_sp <- ggplot(lb_spec %>% filter(FNAMNSVE %in% order$FNAMNSVE[1:12]),
       aes(year, sum_kvant/1000000, fill = FNAMNSVE)) +
  geom_area() +
  scale_fill_brewer(palette = "Set3", name = "") +
  #scale_fill_manual(values = sp_palette, name = "") + 
  theme_sleek(base_size = 9) +
  theme(legend.position = "bottom",
        legend.direction = "horizontal",
        legend.spacing.y = unit(0, 'cm'),
        legend.spacing.x = unit(0.2, 'cm'),
        legend.key.size = unit(0.3, 'cm')) +
  labs(y = "Landningar (tusen ton)", x = "År") + 
  guides(fill = guide_legend(nrow = 2)) +
  coord_cartesian(expand = 0)

ggsave(plot = landings_top_12_sp, "figures/entire_fleet/landings_top_12_sp.png", width = 17, height = 14, units = "cm")

# All species more than 0.001
lb_spec_all <- lb_spec %>% 
  summarise(sum_kvant_mean = mean(sum_kvant), .by = FNAMNSVE) %>% 
  arrange(desc(sum_kvant_mean))

landings_sp <- ggplot(lb_spec_all %>% filter(FNAMNSVE %in% lb_spec_all$FNAMNSVE[1:30]),
       aes(reorder(FNAMNSVE, sum_kvant_mean), sum_kvant_mean/1000000)) +
  geom_bar(stat = "identity", alpha = 0.9) +
  coord_flip(expand = 0) +
  labs(y = "Landningar (tusen ton)", x = "")

ggsave(plot = landings_sp, "figures/entire_fleet/landings_sp.png", width = 17, height = 14, units = "cm")

Fig. 1.4.7 De vanligaste 12 landade arter i det svenska yrkesfisket på kusten och i havet över tid.

Fig. 1.4.8 De vanligaste 30 landade arterna (2016-2022) i det svenska yrkesfisket på kusten och i havet.
# TODO: By value also? all species or just the ones you can actually see?

Landings map for top 12 species

Code 
# Find which are the top 5 species
lb_spec_12 <- lb %>% 
  drop_na(FNAMNSVE) %>% 
  summarise(sum_kvant = sum(KVANT), .by = FNAMNSVE) %>% 
  arrange(desc(sum_kvant))

polygon_counts_sp <- lb_sf %>%
  filter(FNAMNSVE %in% c(lb_spec_12$FNAMNSVE[1:12])) %>% 
  filter(year > 2015) %>% 
  mutate(polygon_id = as.character(st_intersects(geometry, honeycomb_grid_sf))) %>% 
  group_by(polygon_id, FNAMNSVE) %>% # FIXME: want to do it for another year?
  summarise(sum_kvant = sum(KVANT)) %>% 
  st_drop_geometry()

polygon_sum_sp <- honeycomb_grid_sf %>% 
  left_join(polygon_counts_sp, by = "polygon_id") %>% 
  drop_na(sum_kvant)

# Add proportion by species to make the spatial pattern more visible
polygon_sum_sp <- polygon_sum_sp %>% 
  mutate(sum_kvant_prop = sum_kvant / max(sum_kvant), .by = FNAMNSVE)

# All years but Morocco & Arctic excluded for visibility
landings_sp_map <- plot_map_m +
  geom_sf(data = polygon_sum_sp, aes(fill = sum_kvant_prop), color = NA) +
  scale_fill_viridis(trans = "sqrt", option = "plasma",
                     na.value = "yellow",
                     limits = c(0, quantile(polygon_sum_sp$sum_kvant_prop, 0.99))
                     ) +
  geom_sf(color = "gray70") +
  facet_wrap(~FNAMNSVE) +
  # TODO other way to trim plot?
  xlim(-8.2, 25.5) +
  ylim(51.5, 68) +
  guides(fill = "none") + 
  theme(axis.text.x = element_text(angle = 90))

ggsave(plot = landings_sp_map, paste0(home, "/figures/entire_fleet/landings_sp_map.png"), width = 17, height = 17, units = "cm")

Fig. 1.4.9 Utbredning av fiskeplatser (rapporterade landningar) för de 12 ekonomiskt viktigaste arterna i svenska yrkesfisket på kusten och i havet 2019-2022. Skalan är relativ och inte jämförbar mellan arter i absoluta tal
# TODO: Same as above but for top 12 commercially?

Landings for top 12 species by month

Code 
lb_spec <- lb %>% 
  drop_na(FNAMNSVE) %>% 
  summarise(sum_kvant = sum(KVANT), .by = c(month_name, FNAMNSVE))

order <- lb_spec %>% 
  summarise(sum_kvant_all = sum(sum_kvant), .by = FNAMNSVE) %>% 
  arrange(desc(sum_kvant_all)) %>% 
  mutate(sum = sum(sum_kvant_all),
         percent = 100 * (sum_kvant_all / sum)) %>% 
  slice(1:12)

lb_spec <- lb_spec %>% left_join(order, by = "FNAMNSVE")

# Add groups to split plot
lb_spec_split <- lb_spec %>%
  filter(FNAMNSVE %in% order$FNAMNSVE) %>% 
  mutate(sp = ifelse(FNAMNSVE %in% c("Skarpsill", "Tobis", "Sill/Strömming",
                                     "Makrill", "Siklöja", "Blåvitling"), "Pelagic", "Demersal"))
# FIXME: Gråsej? Which group?

# For setting ranges
ymax <- lb_spec_split %>% 
  summarise(month_sum = sum(sum_kvant)/1000000, .by = c(month_name, sp)) %>% 
  group_by(sp) %>% 
  filter(month_sum == max(month_sum))
  
polar_theme <- ggplot() +
  theme(panel.background = element_rect(fill = "white", color = "white"),
        panel.border = element_blank(),
        panel.grid = element_blank(),
        panel.grid.major = element_line(color = "grey95"),
        axis.title = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text.y = element_blank(),
        legend.position = "bottom",
        legend.box.spacing = unit(0, "pt")) +
  coord_polar() +
  guides(fill = guide_legend(nrow = 2))

hlines_pel <- tibble(y = seq(0,
                             ymax %>% filter(sp == "Pelagic") %>% pull(month_sum),
                             length.out = 3)) %>%
  round(digits = -2) %>%
  filter(!y == min(y))

pel <- polar_theme + 
  geom_hline(aes(yintercept = y), hlines_pel, color = "grey80") +
  scale_fill_brewer(palette = "Set3", name = "") +
  scale_y_continuous(limits = c(-50, (ymax %>% filter(sp == "Pelagic") %>% pull(month_sum))*1.1),
                     expand = c(0, 0)) + 
  geom_col(data = lb_spec_split %>% filter(sp == "Pelagic"), 
           aes(x = month_name, y = sum_kvant/1000000, fill = FNAMNSVE)) +
  # Annotate custom scale inside plot
  annotate(x = "December", y = hlines_pel$y[1]*0.8, label = paste(hlines_pel$y[1], "tusen ton"),
           geom = "text", color = "grey50", size = 3) +
  annotate(x = "December", y = hlines_pel$y[2]*0.9, label = paste(hlines_pel$y[2], "tusen ton"),
           geom = "text", color = "grey50", size = 3) + 
  ggtitle("Pelagiska")


hlines_dem <- tibble(y = seq(0,
                             ymax %>% filter(sp == "Demersal") %>% pull(month_sum),
                             length.out = 3)) %>%
  round(digits = -1) %>%
  filter(!y == min(y))

dem <- polar_theme + 
  geom_hline(aes(yintercept = y), hlines_dem, color = "grey80") +
  scale_fill_brewer(palette = "Set3", name = "") +
  scale_y_continuous(limits = c(-5, (ymax %>% filter(sp == "Demersal") %>% pull(month_sum))*1.1),
                     expand = c(0, 0)) + 
  geom_col(data = lb_spec_split %>% filter(sp == "Demersal"), 
           aes(x = month_name, y = sum_kvant/1000000, fill = FNAMNSVE)) +
  # Annotate custom scale inside plot
  annotate(x = "December", y = hlines_dem$y[1]*0.8, label = paste(hlines_dem$y[1], "tusen ton"),
           geom = "text", color = "grey50", size = 3) +
  annotate(x = "December", y = hlines_dem$y[2]*0.9, label = paste(hlines_dem$y[2], "tusen ton"),
           geom = "text", color = "grey50", size = 3) + 
  ggtitle("Demersala")


monthly_polar <- pel + dem

ggsave(plot = monthly_polar, paste0(home, "/figures/entire_fleet/monthly_polar.png"), width = 22, height = 15, units = "cm")

Fig. 1.4.11 Landningsvikt per art och månad av de 12 vanligaste arterna baserat landningskvantitet (2003–2022)
Code 
lb_fish_land_old <- lb %>% 
  filter(year < 2016) %>% 
  summarise(sum_kvant = sum(KVANT)/1000000, .by = fishery) %>% 
  drop_na() %>%
  mutate(tp = "2003-2015") %>% 
  arrange(desc(sum_kvant))

lb_fish_land_new <- lb %>% 
  filter(year > 2015) %>% 
  summarise(sum_kvant = sum(KVANT)/1000000, .by = fishery) %>% 
  drop_na() %>%
  mutate(tp = "2015-2022") %>% 
  arrange(desc(sum_kvant))

fish_land_old <- ggplot(lb_fish_land_old, aes(tp, sum_kvant, fill = factor(fishery, levels = rev(fishery)))) +
  geom_col(position = "stack") +
  scale_fill_viridis(discrete = TRUE, name = "", option = "mako") +
  facet_zoom(ylim = c(sum(lb_fish_land_old[1:4, ]$sum_kvant), sum(lb_fish_land_old$sum_kvant))) +
  geom_hline(yintercept = sum(lb_fish_land_old[1:4, ]$sum_kvant), linetype = 2, linewidth = 0.5, alpha = 0.5) +
  geom_hline(yintercept = sum(lb_fish_land_old$sum_kvant), linetype = 2, linewidth = 0.5, alpha = 0.5) +
  theme(strip.background = element_rect(fill = "grey70", colour = "grey70"),
        legend.position = "bottom") +
  labs(x = "", y = "Landningar (tusen ton)") +
  guides(fill = guide_legend(ncol = 2)) +
  NULL

ggsave(plot = fish_land_old, paste0(home, "/figures/entire_fleet/fish_land_old.png"), width = 17, height = 17, units = "cm")

fish_land_new <- ggplot(lb_fish_land_new, aes(tp, sum_kvant, fill = factor(fishery, levels = rev(fishery)))) +
  geom_col(position = "stack") +
  scale_fill_viridis(discrete = TRUE, name = "", option = "mako") +
  facet_zoom(ylim = c(sum(lb_fish_land_new[1:4, ]$sum_kvant), sum(lb_fish_land_new$sum_kvant))) +
  geom_hline(yintercept = sum(lb_fish_land_new[1:4, ]$sum_kvant), linetype = 2, linewidth = 0.5, alpha = 0.5) +
  geom_hline(yintercept = sum(lb_fish_land_new$sum_kvant), linetype = 2, linewidth = 0.5, alpha = 0.5) +
  theme(strip.background = element_rect(fill = "grey70", colour = "grey70"),
        legend.position = "bottom") +
  labs(x = "", y = "Landningar (tusen ton)") +
  guides(fill = guide_legend(ncol = 2)) +
  NULL

ggsave(plot = fish_land_new, paste0(home, "/figures/entire_fleet/fish_land_new.png"), width = 17, height = 17, units = "cm")

Fig. 1.4.16 Svenskt yrkesfiske i havet och på kusten, landningar per fiskeri (2016-2022)

Fig. 1.4.16 Svenskt yrkesfiske i havet och på kusten, landningar per fiskeri (2003-2015)
Code 
order <- lb %>% 
  filter(year > 2015) %>% 
  drop_na(FNAMNSVE) %>% 
  summarise(sum_kvant_all = sum(KVANT), .by = FNAMNSVE) %>% 
  mutate(sum = sum(sum_kvant_all),
         percent = 100 * (sum_kvant_all / sum)) %>% 
  arrange(desc(percent)) %>% 
  slice(1:12)

lb_fish_sp <- lb %>% 
  filter(!year == 2022) %>% 
  mutate(species = ifelse(!FNAMNSVE %in% order$FNAMNSVE[1:12], " Övriga", FNAMNSVE)) %>% 
  summarise(sum_kvant = sum(KVANT)/1000000, .by = c(fishery, species)) %>% 
  drop_na()

pal_sp <- brewer.pal(n = 12, name = "Set3")

#sp_palette_other <- c(sp_palette, Övriga = "Black")

fish_sp <- ggplot(lb_fish_sp, 
                  aes(sum_kvant, fishery, fill = species)) +
  geom_bar(stat = "identity") + 
  scale_fill_manual(values = c("black", pal_sp), name = "") +
  #scale_fill_manual(values = sp_palette_other, name = "") +
  scale_x_continuous(transform = "sqrt") +
  labs(y = "Landningar (tusen ton)", x = "") + 
  guides(fill = guide_legend(nrow = 4)) + 
  theme(legend.position = "bottom",
        plot.margin = unit(c(0.1, 0.5, 0.1, 0.1), "cm"))

ggsave(plot = fish_sp, paste0(home, "/figures/entire_fleet/fish_sp.png"), width = 17, height = 12, units = "cm")

Fig. 1.5.3. Kvantitet och artsammansättning i olika fisken (2016-2022). Notera att x-axeln är sqrt

Discards

All fisheries

Code 
discard_sum <- discard %>% 
  summarise(tonnes = sum(discards) / 1000000, .by = c(year, fishery)) %>% 
  mutate(type = "Utkast") %>% 
  mutate(keep = ifelse(year == 2015 & fishery == "Bottentrålfiske efter torsk i Östersjön", "N", "Y")) %>% 
  filter(keep == "Y") %>% 
  dplyr::select(-keep)

# For the fisheries I have discard from, what are the landings
landing_sum <- lb %>% 
  filter(fishery %in% unique(discard_sum$fishery)) %>%
  filter(year %in% unique(discard_sum$year)) %>% 
  summarise(tonnes = sum(KVANT) / 1000000, .by = c(year, fishery)) %>% 
  mutate(type = "Landningar") %>% 
  mutate(keep = ifelse(year == 2015 & fishery == "Bottentrålfiske efter torsk i Östersjön", "N", "Y")) %>% 
  filter(keep == "Y") %>% 
  dplyr::select(-keep)
  
discard_sum <- discard_sum %>% bind_rows(landing_sum)

# Plot by fishery over time
discard_all_plot <- ggplot(discard_sum %>% filter(!year %in% c(2020, 2021)),
                           aes(year, tonnes, fill = factor(type, levels = c("Utkast", "Landningar")))) +
  geom_bar(stat = "identity", alpha = 0.9) + 
  geom_text(data = md, size = 2.5, 
            aes(year, y, label = label), inherit.aes = FALSE, angle = 90,
            color = "grey50", hjust = -0.05) +
  scale_fill_manual(values = c("gray20", "gray80"), name = "") + 
  theme_sleek(base_size = 9) +
  theme(legend.position = c(0.75, 0.14),
        legend.direction = "horizontal",
        legend.spacing.y = unit(0, 'cm'),
        legend.spacing.x = unit(0.2, 'cm'),
        legend.key.size = unit(0.3, 'cm')) +
  labs(y = "Fångst (tusen ton)", x = "") + 
  guides(fill = guide_legend(nrow = 3)) +
  facet_wrap(~fishery, ncol = 2, scales = "free_y") +
  coord_cartesian(expand = 0)
  
ggsave(plot = discard_all_plot, "figures/entire_fleet/discards.png", width = 17, height = 16, units = "cm")

Fig. 1.5.2. Utkast per fiske (2008-2022).

Discard by species

Code 
# All species more than 0.001
discard_sum <- discard %>% 
  summarise(sum_disc = sum(discards), .by = Art) %>% 
  arrange(desc(sum_disc)) %>% 
  slice(1:30)

discard_sp <- ggplot(discard_sum,
       aes(reorder(Art, sum_disc), sum_disc/1000000)) +
  geom_bar(stat = "identity", alpha = 0.9) +
  coord_flip(expand = 0) +
  labs(y = "Utkast (tusen ton)", x = "")

ggsave(plot = landings_sp, "figures/entire_fleet/discard_sp.png", width = 17, height = 14, units = "cm")

Fig. 1.5.2. Utkast per art.

Proportion of catches that are discards

Code 
discard_trends <- discard %>% 
  summarise(sum_disc = sum(discards),
            sum_kvant = sum(KVANT), .by = c(Art, year)) %>% 
  mutate(prop = sum_disc / (sum_kvant + sum_disc)) %>% 
  arrange(desc(sum_disc)) %>% 
  drop_na(prop) %>% 
  filter(Art %in% discard_sum$Art) %>% 
  # remove species with lots of gaps in time
  mutate(n_year = length(unique(year)), .by = Art) %>% 
  filter(n_year > 8)

discard_ratio_heat <- ggplot(discard_trends, aes(year, reorder(Art, prop), fill = prop)) +
  geom_tile(color = "white") +
  scale_fill_viridis(option = "magma") +
  geom_text(data = md %>% filter(fishery == head(fishery, 1)), size = 3.5, 
            aes(year, Inf, label = label), inherit.aes = FALSE, angle = 90,
            color = "grey50", hjust = 2) +
  coord_cartesian(expand = 0) +
  labs(y = "", x = "År", fill = "Andel utkast av fångst") + 
  guides(fill = guide_colorbar(title.position = "top", title.hjust = 0.5)) +
  theme(legend.position = "bottom", 
        legend.key.width = unit(1, "cm"))

ggsave(plot = discard_ratio_heat, "figures/entire_fleet/discard_ratio_heat.png", width = 17, height = 11, units = "cm")

1.5.2. Andel utkast över tid per art för arter med mer än 8 år av data.

Fishery-specific plots

Loop through all fisheries and make standard plots

Code 
lb_clean <- lb %>%
  drop_na(fishery)

for(i in unique(lb_clean$fishery)) {
  
  lb_sub <- lb_clean %>% filter(fishery == i)
  
  discard_sub <- discard %>% filter(fishery == i)
  
  lb_spec_key <- lb_sub %>% 
    drop_na(FNAMNSVE) %>% 
    summarise(sum_kvant = sum(KVANT), .by = FNAMNSVE) %>% 
    arrange(desc(sum_kvant)) %>% 
    mutate(sum = sum(sum_kvant),
           percent = (sum_kvant / sum) * 100) %>% 
    filter(percent > 0.5) %>% # both percent and top 5 if more than 5...
    slice(1:5)
  
  # Map plot
  polygon_counts_sub <- lb_sf %>%
    mutate(tp = ifelse(year > 2015, "2016-2022", "2003-2015")) %>% 
    mutate(polygon_id = as.character(st_intersects(geometry, honeycomb_grid_sf))) %>%
    filter(fishery == i) %>%
    filter(FNAMNSVE %in% lb_spec_key$FNAMNSVE) %>%
    group_by(tp, polygon_id, FNAMNSVE) %>%
    summarise(sum_kvant = sum(KVANT) / 1000000) %>%
    st_drop_geometry()
  
  polygon_sum_sub <- honeycomb_grid_sf %>%
    left_join(polygon_counts_sub, by = "polygon_id") %>%
    drop_na(sum_kvant)
  
  plot_map_l +
    geom_sf(data = polygon_sum_sub, aes(fill = sum_kvant), color = NA) +
    scale_fill_viridis(trans = "sqrt", option = "plasma",
                       na.value = "yellow",
                       limits = c(0, quantile(polygon_sum_sub$sum_kvant, 0.99)),
                       name = "Landningar (tusen ton)") +
    geom_sf(color = "gray70") +
    facet_grid(tp ~ FNAMNSVE) +
    # xlim(-8.2, 25.5) +
    # ylim(51.5, 68) +
    # Use limits closer to data
    xlim(c(min(lb_sub$lon), max(lb_sub$lon))) +
    ylim(c(min(lb_sub$lat), max(lb_sub$lat))) +
    guides(fill = guide_colorbar(title.position = "top", title.hjust = 0.5)) +
    theme(axis.text.x = element_text(angle = 90),
          legend.text = element_text(size = 6),
          legend.title = element_text(size = 7),
          legend.position = "bottom",
          legend.key.height = unit(0.2, "cm"),
          legend.key.width = unit(0.8, "cm")) +
    ggtitle(i)
  
  ggsave(paste0(home, "/figures/fishery/", i, "/", "_map.png"),
         width = 17, height = 17, units = "cm")

  
  
  # Fleet size-distribution
  trend_sub <- lb_sub %>% 
    drop_na(LÄNGD) %>% 
    filter(LÄNGD > 0) %>% 
    mutate(size_gr = ifelse(LÄNGD > 10, ">10 m", "<10 m")) %>% 
    summarise(n_vessels = length(unique(BATNAMN)), .by = c(year, size_gr))
  
  ggplot(trend_sub, aes(year, n_vessels, fill = size_gr)) + 
    geom_area() + 
    coord_cartesian(expand = 0) +
    scale_fill_manual(values = c("grey40", "grey70"), name = "Fartygslängd") + 
    labs(y = "Antal fartyg", x = "År") + 
    theme(legend.position = "bottom") +
    guides(color = guide_legend(title.position = "top", title.hjust = 0.5)) +
    ggtitle(i)
    
    ggsave(paste0(home, "/figures/fishery/", i, "/", "_size.png"),
           width = 17, height = 11, units = "cm")
  
    
  
  # Landings/effort by species and fishery 
  lb_spec_sub <- lb_sub %>% 
    drop_na(FNAMNSVE) %>% 
    summarise(sum_kvant = sum(KVANT), .by = c(year, FNAMNSVE))
  
  order_sub <- lb_spec_sub %>% 
    summarise(sum_kvant_all = sum(sum_kvant), .by = FNAMNSVE) %>% 
    arrange(desc(sum_kvant_all)) %>% 
    mutate(sum = sum(sum_kvant_all),
           percent = (sum_kvant_all / sum) * 100) %>% 
    filter(percent > 0.5)
  
  lb_spec_sub <- lb_spec_sub %>% left_join(order_sub, by = "FNAMNSVE")
  
  ggplot(lb_spec_sub %>% filter(FNAMNSVE %in% order_sub$FNAMNSVE[1:12]),
         aes(year, sum_kvant/1000000, fill = FNAMNSVE)) +
    #geom_bar(stat = "identity") +
    geom_area() +
    scale_fill_brewer(palette = "Set3", name = "") + 
    #scale_fill_manual(values = sp_palette, name = "") + 
    theme_sleek(base_size = 9) +
    theme(legend.position = "bottom",
          legend.direction = "horizontal",
          legend.spacing.y = unit(0, 'cm'),
          legend.spacing.x = unit(0.2, 'cm'),
          legend.key.size = unit(0.4, 'cm')) +
    labs(y = "Landningar (tusen ton)", x = "År") + 
    guides(fill = guide_legend(nrow = 2)) +
    coord_cartesian(expand = 0) +
    ggtitle(i)
  
  ggsave(paste0(home, "/figures/fishery/", i, "/", "_species.png"),
         width = 17, height = 11, units = "cm")
  
  
  
  # Seasonal distribution of fisheries
  lb_spec_month_sub <- lb_sub %>% 
    summarise(sum_kvant = sum(KVANT), .by = c(month_name, FNAMNSVE))
  
  order_sub <- lb_spec_month_sub %>% 
    summarise(sum_kvant_all = sum(sum_kvant), .by = FNAMNSVE) %>% 
    arrange(desc(sum_kvant_all)) %>% 
    mutate(sum = sum(sum_kvant_all),
           percent = 100 * (sum_kvant_all / sum)) %>% 
    filter(percent > 0.5) %>%
    slice(1:12)
    
  lb_spec_month_sub <- lb_spec_month_sub %>% 
    filter(FNAMNSVE %in% order_sub$FNAMNSVE)
  
  lb_spec_month_sub <- lb_spec_month_sub %>% left_join(order, by = "FNAMNSVE")
  
  ymax <- lb_spec_month_sub %>% 
    filter(FNAMNSVE %in% order_sub$FNAMNSVE) %>% 
    summarise(month_sum = sum(sum_kvant)/1000, .by = month_name) %>% 
    filter(month_sum == max(month_sum)) %>% 
    pull(month_sum)
  
  hlines <- tibble(y = seq(0, ymax, length.out = 4)) %>% round(digits = -2) %>% filter(!y == min(y))
  
  polar_theme + 
    geom_hline(aes(yintercept = y), hlines, color = "grey80") +
    scale_fill_brewer(palette = "Set3", name = "") + 
    #scale_fill_manual(values = sp_palette, name = "") + 
    scale_y_continuous(limits = c(-50, ymax), expand = c(0, 0)) + 
    geom_col(data = lb_spec_month_sub, 
             aes(x = month_name, y = sum_kvant/1000, fill = FNAMNSVE), alpha = .95) +
    # Annotate custom scale inside plot
    annotate(x = "December", y = hlines$y[1]*0.8, label = paste(hlines$y[1], "ton"),
             geom = "text", color = "grey50", size = 3) +
    annotate(x = "December", y = hlines$y[2]*0.9, label = paste(hlines$y[2], "ton"),
             geom = "text", color = "grey50", size = 3) +
    annotate(x = "December", y = hlines$y[3]*0.93, label = paste(hlines$y[3], "ton"),
             geom = "text", color = "grey50", size = 3) +
    ggtitle(i)
  
  ggsave(paste0(home, "/figures/fishery/", i, "/", "_season.png"),
         width = 15, height = 15, units = "cm")

  
  
  # landings/effort by gears or sub-fisheries within fishery 
  lb_gear_sub <- lb_sub %>% 
    drop_na(Redskap2) %>% 
    summarise(sum_kvant = sum(KVANT), .by = c(year, Redskap2))
  
  ggplot(lb_gear_sub, aes(year, sum_kvant/1000, fill = Redskap2)) +
    geom_area() +
    #geom_bar(stat = "identity") +
    scale_fill_manual(values = pal_long, name = "") + 
    theme_sleek(base_size = 9) +
    theme(legend.position = "bottom",
          legend.direction = "horizontal",
          legend.spacing.y = unit(0, 'cm'),
          legend.spacing.x = unit(0.2, 'cm'),
          legend.key.size = unit(0.4, 'cm')) +
    labs(y = "Landningar (ton)", x = "") + 
    guides(fill = guide_legend(nrow = 2)) +
    coord_cartesian(expand = 0) +
    ggtitle(i)
  
  ggsave(paste0(home, "/figures/fishery/", i, "/", "_gear.png"),
         width = 17, height = 11, units = "cm")
  
  
  
  # Discard species by fishery
  discard_sub <- discard_sub %>% 
    mutate(fishery_discards_sum = sum(discards), .by = fishery) %>% 
    mutate(percent = (discards / fishery_discards_sum) * 100, .by = Art) %>% 
    filter(percent > 0.5) %>% 
    summarise(discards = sum(discards) / 1000, .by = c(year, Art))
  
  # The reason we dont do slice typ 12 is because we want it per gear 
  nsp <- length(unique(discard_sub$Art))
  colors <- colorRampPalette(brewer.pal(name = "Set3", n = 12))(nsp)
  
  ggplot(discard_sub, aes(year, discards, fill = Art)) +
    geom_bar(stat = "identity") +
    geom_text(data = md %>% filter(fishery == i), size = 2.5, 
              aes(year, y, label = label), inherit.aes = FALSE, angle = 90,
              color = "grey50", hjust = -0.05) +
    scale_fill_manual(values = colors) +
    #scale_fill_manual(values = sp_palette) + 
    theme_sleek(base_size = 9) +
    theme(legend.position = "bottom",
          legend.direction = "horizontal",
          legend.spacing.y = unit(0, 'cm'),
          legend.spacing.x = unit(0.2, 'cm'),
          legend.key.size = unit(0.3, 'cm')) +
    labs(y = "Utkast (ton)", x = "") + 
    guides(fill = guide_legend(nrow = 2, title.position = "top", title.hjust = 0.5)) +
    scale_y_continuous(expand = c(0, 0)) + 
    scale_x_continuous(breaks = c(2012, 2016, 2020)) +
    ggtitle(i)
  
    ggsave(paste0(home, "/figures/fishery/", i, "/", "_discards.png"),
           width = 17, height = 11, units = "cm")

}

Active gears

Pelagic fishing with active gear

# TODO: coastal/pelagic

Shrimp fisheries in Skagerrak, Kattegatt and the North Sea

# TODO: gear selectivity?
# TODO: protected areas
# TODO: gear selectivity?
# TODO: Kattegatt & Skagerak separately

Bottom trawl fisheries for nephrops and fish in Skagerrak and Kattegatt

![](figures/fishery/Bottentrålfiske efter kräfta och fisk i Skagerrak och Kattegatt/_discards.png

# TODO: Size distribution of discards by gear selectivity? Check the efforts in Baltic Sea

Bottom trawl fisheries for nephrops and fish in the North Sea

Vendace fisheries with bottom trawl

Bottom trawl fisheries for cod in the Baltic Sea

# TODO: Landings by stock

Passive gears

Passive gear fisheries in the Baltic Sea

Passive gear fisheries in the northern Baltic Sea

Fisheries in the Öresund

Passive gear fisheries on the West coast

Fisheries with nephrops creels

Salmon fisheries